Benchmark / Failure Learning

A benchmark should test what happens after the first failure.

WisdomBench is framed here for readers searching for AI benchmarks, LLM evaluation, failure learning, and recovery under repeated feedback.

AI benchmark LLM evaluation AI evaluation failure learning benchmark AI learning from mistakes WisdomBench

Evidence Map Papers Registries Counterexamples

Search Intent

The search intent is not 'which model wins once' but 'which system learns from failure without cheating the evidence'.

How do you evaluate AI after repeated failures?
What is a benchmark for learning from mistakes?
How is wisdom different from single-shot capability?
What evidence prevents benchmark overclaiming?

Benchmark Target

Failure learning is not the same as first-try success.

Many benchmarks measure whether a model can answer or act successfully on first exposure. A failure-learning benchmark asks what happens after contradiction, correction, drift, and repeated repair.

The relevant question is whether the system improves without hiding failure, changing the task, leaking credit, or broadening the claim beyond the evidence.

Evidence

A benchmark needs a failure standard and a credit boundary.

The public boundary is explicit: a better score is not enough unless the evaluation states what counts as failure, repair, leakage, and unsupported transfer.

That is why each benchmark claim should route back to DOI, dataset, registry, or counterexample evidence.

Evidence Route

Where the claim can be checked.

This page is an entry point. The claim should be evaluated through DOI records, evidence maps, registries, GitHub/HF technical routes, and public counterexamples.

KindAnchorURLRole

Evidence MapPublic claim and evidence maphttps://mianzhang.org/evidence/Start from supported claims and known boundaries.

Paper IndexDOI and paper status maphttps://mianzhang.org/papers/Use paper-specific DOI records for paper claims.

RegistriesMachine-readable public registrieshttps://mianzhang.org/registries/Inspect claim, evidence, action, and counterexample records.

Challenge RouteCounterexample submission pathhttps://mianzhang.org/counterexamples/Attack overbroad claims through public routes.

ArchiveZenodo portfolio indexhttps://zenodo.org/records/20027295Long-term archive index; cite specific DOI records when available.

ConceptWisdomBenchhttps://mianzhang.org/concepts/wisdombench.htmlStable concept definition and evidence scope.

DatasetWisdomBench Hugging Face datasethttps://huggingface.co/datasets/MMJBDS/wisdombenchTechnical dataset route.

Boundary

What this page does not prove.

This page does not claim universal SOTA performance.
It does not claim that one benchmark proves general intelligence or general wisdom.
It does not turn a dataset card into a production guarantee.

FAQ